Bio materials characterization with NIR

1. Bio-materials characterization with NIR
Anna Sandak & Jakub Sandak

2. 2
Outline
Why spectroscopy?
Tendencies in biomaterials
developmnet
NIR in bio-materials characterization
Trends in NIR applications

3. 3
Goals
• to exploit the potential of near infrared
spectroscopy
• to demonstrate its capabilities for bio-based
materials characterization
• to highlight the potential of the NIR as a tool
capable of providing complementary
information to other techniques
• to present current trends in NIR developments

4. 4
Bio-materials in construction sector
• In Italy 1 on 12 buildings is made of wood and
growing tendency is observed nowadays
• Bio-based materials are often used for
retrofitting of existing structures, upward
construction or vertical gardens
• Buildings that use bio-materials are not just
sustainable, strong and durable;
they are also beautiful

5. 5
Development priorities
• Structural components
(need for developed wood products -
Engineered Wood Products, high strength wood,
moisture resistant sills, light-weight
beams/joists/studs of bio-composites, sandwich
panels for exterior walls)
• Insulation
(need for compactable bats of cellulose
insulation, environmentally friendly fire
impregnation, high-performance insulation that
provides thinner walls, insulation, optimized for
soundproofing)
• Barrier Materials
(need for bio-based wind and vapor barrier for
moisture-proof exterior walls, waterproofing for
wet areas, façade and roofing materials with
improved durability/serviceability
Source: Per-Erik Eriksson: Future sustainable biobased buildings

6. 6
Durability and performance

7. 7
Surface properties
roughness
color
brighntess
surface free energy
wetability
gloss
pattern
temperaturesoftiness
hardness
touch experience
facture
waviness
chemical activity
contamination
resistance to dirt
outlook
resistance to scratch
resistance to abrassion
adhesion
soft feel
aseptic
durability
leveling
water repellency
matting effect

8. 8
How to assess properties?

9. 9
Why NIR spectroscopy?
 No need special sample preparation
 Non-destructive testing
 Relatively fast measurement
 No residues/solvents to waste
 Determination of many components simultaneously
 High degree of precision and accuracy
 Direct measurement with very low cost
 Not self standing technology
 Overlapping of spectral peaks
 Not straightforward interpretation
 Needs statistics methods for data analysis

10. 10
chemical industry, microanalysis,
pharmaceutical analysis, soil,
polymers, food and beverages,
surface science, fuels, textile
industry, art conservation,
forensics, PAT, remote sensing
and also wood and paper
industry
NIR applications

11. 11
…but what can we see in wood?
http://www.ifb.ethz.ch/research/sinergia

12. 12
Cellulose (40-50%)
• linear polymer
• long chain
(DP 5000-10000)
• Β-D-Glucopyranose
units
• 1,4-glycosidic bonds
• hydroxyl groups
bonds
• different kind in
wood:
• - amorphous,
- semi-crystalline,
- crystalline
methylene groups
hydroxyl groups

13. 13
Lignin (20-30%)
Softwood (guaiacyl units) Hardwood (guaiacyl + syringyl units)
methoxyl groups
phenolic hydroxyl groups
aldehyde groups
carbonyl groups

14. 14
Hemicellulose (25-35%)
xylan
• hetero-polysaccharides
• short chain
(DP 150-200)
• amorphous
• high reactivity
• various composition
for hardwood and
softwood
acetyl groups
hydroxyl groups

15. 15
NIR & biomaterials characterization
• Chemical compositions
(cellulose, lignin, extractives)
• Physical and anatomical characterization
(density, moisture, calorific value, microfibryl angle, defects
detection, sapwood/heartwood detection)
• Mechanical properties
(compression, tension, MOE, MOR)
• Identification and monitoring others properties of wood
(decay monitoring, weathering, waterlogging, thermal
treatment, chemical modifications)
• Paper industry
(weight, thickness, moisture, Kappa number, mechanical
resistance, type of pulp/paper)

16. 16
Virgin wood
• Species recognition
• Wood provenance
• Exotic wood evaluation
• Biomass characterization
• Clones recognition

17. 17
Recognition of wood species
1 21 31 9
41 1
51 61 71
81 91 10 11 121 131 9
141 9
151 161 171 181 191 20
softwood hardwood exotic wood
01- Picea abies, 02-Abies alba, 03-Pinus cembra, 04-Larix decidua, 05-Pinus sylvestris, 06-Pinus ponderosa, 07-
Tsuga, 08- Thuja sp., 09-Quercus robur, 10-Robinia pseudoacacia, 11-Castanea sativa, 12-Iroko, 13-Afrormosia,
14-Okumè, 15-Sipo, 16-Meranti, 17-Wengè, 18-Azobè, 19-Ipè, 20-Itauba, 21-Pau Rosado, 22-Teck, 23-Bangkirai

18. 18
Use of CA for species classification
Picea abies Pinus cembra Larix sp. Thuja plicata Abies alba Pinus silvestris Tsuga sp.
investigatedsample

19. 19
Wood provenance & NIRS
2163 trees of Norway spruce
from 75 location
in 14 European countries
2163 samples measured
x 5 spectra/sample
= 10815 spectra

20. 20
Differentation of origin
samples form 14 countries (75 locations); totally 10815 spectra was analyzed and evaluated
 Austria ▲ Bulgaria ▼ Czech Republic ■ Croatia ■ Estonia  Finland  France
 Germany  Hungary  Italy  Norway  Poland  Romania  Sweden

21. 21
Italy
TRENTO
Moena
Cavalese












Garda lake

22. 22






Principal Component Analysis
PCA of spectra of wooden samples coming from six different locations (14 sites) in
Trentino region
Note: second derivative, vector normalization, method: factorization (5 factors)
region: 9590-5250cm-1, 5100-4160cm-1
 Strada Alta Valcigolera
 Strada Orti Forestali,
 Juribello
 Paneveggio
 Cadino 06
 Cadino 43
 Cadino 49
 Folgaria 04
 Folgaria 22
 Monte Sover 7
 Monte Sover 11
 Monte Sover 14
 Monte Sover 69
 Caoria Valsorda

23. 23
Cluster Analysis
CADINO  CADINO CADINO 
CA – wooden samples from 3 different districts of Cadino valley 
Note: second derivative, vector normalization, Ward’s algorithm, region: 7089-5072cm-1

24. 24
coconut shell tamarind grass
Identification of agricultural residues

25. Particleboards characterization
25
A
B
C
Raw resources: Eastern redcedar (Juniperus virginiana L.)
Single- and three-layers panels
manufactured from raw material using 9% urea formaldehyde, combination of 15%
modified corn starch and 2% urea formaldehyde adhesive
12345678910111213141516171819
-0,008
0
0,008
40004500500055006000650070007500
wavenumber (cm-1)
PC2,PC3
PC2
PC3

26. 26
Selection of biomass
for optimal bio-conversion process
PCA of willow clones. Note: pre-processing:
2nd derivative + vector normalization,
17 smoothing points, region: 6869-5847 cm-1,
method: factorization, 3 factors
duotur
sprint
turbo
wodtur
monoturstart
oltur
tur PC3
PC4
PC2

27. 27
Willows utilization
willows
thermo-chemical
conversion
chemical
conversion
mechanical-chemical
conversion
combustion pyrolysis gasification fermentation
anaerobic
digestion
estrificationpulping
panel
production
heat syngas pulp&paper fiber-panel ethanol biogas biodieselcharcoalsyngasbio-oil
high lignin content high carbohydrate content
high fiber
content
extraction
high extractives content acumulation of heavy metalsfast
resprouting
manufacturing environmental
engineering
phytominig
phyto-
remediation
biofiltrationfurniturebasketry
metalsclean soilclean waterchairscontainers
cosmetic foodpharmaceutic
cream
anti-
phlogistic
anti-
pyretic preservative
anti-
oxidantshampoo
cultivation
apiculture
honeypollination
construction
green
architecture
fencesroadmats
fast growingearly flowering

28. 28
Suggested conversion path
thermo-
chemical
mechano-
chemical
chemical
pharmaceut
ical
phytoremediation
#1 
0.93

0.07

0.13

0.96

0.19

-0.10

0.00
#2 
0.98

0.04

-0.01

0.98

0.44

-0.03

0.02
#3 
0.97

0.13

0.01

1.02

0.57

0.88

0.33
#4 
1.05

-0.07

1.04

0.99

0.61

0.99

0.20
#5 
0.89

0.13

0.19

0.80

0.57

-0.06

0.19
#6 
0.05

-0.05

-0.13

1.14

0.68

0.15

0.42
#7 
-0.08

0.78

0.02

0.03

0.33

-0.07

0.50
#8 
0.09

1.10

-0.07

-0.11

0.06

0.00

-0.03
#9 
-0.05

0.90

-0.06

0.02

0.28

0.15

0.30
#10 
-0.03

0.04

0.07

0.03

0.09

0.14

-0.11
#11 
-0.02

-0.05

1.01

-0.06

1.19

0.84

0.31
#12 
-0.04

-0.07

1.05

-0.04

1.32

-0.15

0.22
#13 
-0.02

-0.04

0.93

0.08

1.22

0.32

0.35
#14 
-0.04

-0.04

0.92

0.10

1.00

0.86

0.66
#15 
1.11

0.95

0.84

0.00

1.04

0.09

0.11
#16 
0.16

1.12

-0.02

0.06

1.31

1.12

0.60
#17 
0.06

0.06

1.07

0.01

1.10

-0.14

-0.05
high content of: high accumulation of:
Discrimin
ation rule
high HHV
cellulose
hemicelluos
e
extractives Zn Pb Cu
Threshold
rule
HHVexp>20.0 Xc>38% Xhe>34% Xe>11% XZn>55ppb XPb>8ppb XCu>8ppb
Average
prediction
error
0.06 0.08 0.07 0.06 0.29 0.11 0.29http://www.seco.cpa.state.tx.us/publications/renewenergy/biom
assenergy.php

29. 29
Chemical analysis
• cellulose (by Browning)
• holocellulose (by Browning)
• lignin (T 222 om-06 TAPPI)
• pentosans (T 223 cm-01 TAPPI)
• hot water extractives (T 207 cm-08 TAPPI)
• cold water extractives (T 207 cm-08 TAPPI)
• 1% NaOH extractives (T 212 om-07 TAPPI)
• organic solvent extractives (T 204 cm-07 TAPPI)

30. 30
NIR PLS calibration models #1
of willows chemical composition (pilot study)
22
23
24
25
26
27
28
22 23 24 25 26 27 28
lignin measured (%)
ligninpredicted(%)
42
43
44
45
46
47
48
49
50
51
52
42 43 44 45 46 47 48 49 50 51 52
cellulose measured (%)
cellulosepredicted(%)
17
18
19
20
21
17 18 19 20 21
pentosans measured (%)
pentosanspredicted(%)
70
71
72
73
74
75
76
77
78
79
80
70 71 72 73 74 75 76 77 78 79 80
holocellulose measured (%)
holocellulosepredicted(%)
lignin
r2
= 0.969
RMSECV = 0.24
RPD = 5.7
cellulose
r2
= 0.9 82
RMSECV = 0. 39
RPD = 7.4
pentosans
r2
= 0.96 7
RMSECV = 0.11
RPD = 5.5
holocellulose
r2
= 0.968
RMSECV = 0.33
RPD = 5.6

31. 31
NIR PLS calibration models #2
of willows chemical composition (pilot study)
18
19
20
21
22
23
24
25
26
27
28
18 19 20 21 22 23 24 25 26 27 28
soluble in 10% NaOH measured (%)
solublein10%NaOHpredicted(%)
2
3
4
5
6
2 3 4 5 6
soluble in organic measured (%)
solubleinorganicpredicted(%)
0
1
2
3
4
0 1 2 3 4
soluble in cold water measured (%)
solubleincoldwaterpredicted(%)
0
1
2
3
4
5
6
7
0 1 2 3 4 5 6 7
soluble in hot water measured (%)
solubleinhotwaterpredicted(%)
soluble in cold water
r2
= 0.983
RMSECV = 0.20
RPD = 7.7
soluble in hot water
r2
= 0.983
RMSECV = 0.12
RPD = 7.6
soluble in organic solvents
r2
= 0.981
RMSECV = 0.12
RPD = 7.2
soluble in 10% NaOH
r2
= 0.965
RMSECV = 0.37
RPD = 5.4
18
19
20
21
22
23
24
25
26
27
28
18 19 20 21 22 23 24 25 26 27 28
soluble in 10% NaOH measured (%)
solublein10%NaOHpredicted(%)
18
19
20
21
22
23
24
25
26
27
28
18 19 20 21 22 23 24 25 26 27 28
soluble in 10% NaOH measured (%)
solublein10%NaOHpredicted(%)
2
3
4
5
6
2 3 4 5 6
soluble in organic measured (%)
solubleinorganicpredicted(%)
2
3
4
5
6
2 3 4 5 6
soluble in organic measured (%)
solubleinorganicpredicted(%)
0
1
2
3
4
0 1 2 3 4
soluble in cold water measured (%)
solubleincoldwaterpredicted(%)
0
1
2
3
4
5
6
7
0 1 2 3 4 5 6 7
soluble in hot water measured (%)
solubleinhotwaterpredicted(%)
soluble in cold water
r2
= 0.983
RMSECV = 0.20
RPD = 7.7
soluble in hot water
r2
= 0.983
RMSECV = 0.12
RPD = 7.6
soluble in organic solvents
r2
= 0.981
RMSECV = 0.12
RPD = 7.2
soluble in 10% NaOH
r2
= 0.965
RMSECV = 0.37
RPD = 5.4
0
1
2
3
4
0 1 2 3 4
soluble in cold water measured (%)
solubleincoldwaterpredicted(%)
0
1
2
3
4
0 1 2 3 4
soluble in cold water measured (%)
solubleincoldwaterpredicted(%)
0
1
2
3
4
5
6
7
0 1 2 3 4 5 6 7
soluble in hot water measured (%)
solubleinhotwaterpredicted(%)
0
1
2
3
4
5
6
7
0 1 2 3 4 5 6 7
soluble in hot water measured (%)
solubleinhotwaterpredicted(%)
soluble in cold water
r2
= 0.983
RMSECV = 0.20
RPD = 7.7
soluble in hot water
r2
= 0.983
RMSECV = 0.12
RPD = 7.6
soluble in organic solvents
r2
= 0.981
RMSECV = 0.12
RPD = 7.2
soluble in 10% NaOH
r2
= 0.965
RMSECV = 0.37
RPD = 5.4

32. 32
Wood modifications & NIRS
• Thermal treatment
• Decay
• ISPM-15
• Densification
• Mechanical testing
• Wood after wood machining
• Coatings
• Weathering
• Service life prediction
• …

33. 33
Wood thermal treatment
fir spruce larch
parameter ML EMC ML EMC ML EMC
range (cm-1) 7502-6098
5450-4246
6102-4246 10996-10098
9303-8501
5018-4435
8505-4435 9403-5446 9403-7498
6102-5446
pre-processing 1der +MSC 1 der + VN VN 1 der + VN 1 der + VN 1 der +MSC
r2 98.67 97.58 98.48 98.83 97.88 97.69
RMSECV 0.26 0.25 0.25 0.17 0.39 0.19
RPD 8.66 6.42 8.11 9.42 6.87 6.58
rank 3 3 3 4 4 5
Termovuoto: open system:
(volatile products of the process are
continuously removed from the kiln by
means of a vacuum pump)
The treatment time: 3 hours
Pressure: 0.2 bar
treatment temperatures of 160, 180,
200 and 220ºC.
+
5hardwoods…
195 samples measured x 5 spectra/sample = 975 spectra

34. 34
New spectra presentation
Wild cherry
(Prunus avium)
no treated 160ºC 180ºC 200ºC 220ºC
-1,25
-1
-0,75
-0,5
-0,25
0
am. cell OH
cell CH CH2
cell CH2
cell CO OH CH2
cell OH
cell OH CH CH2
cell OH CO
cryst. cell OH
cryst. cell CH
cryst. cell OH CH
cell hemi CH
cell hemi CH CH2 OH CO
cell hemi OH CH
cell hemi lig CH
extr. CH
hemi C=O
hemi CH
hemi CH
hemi OH
hemi CH
hemi lig extr. CH C=C C=O
lig CH
lig CH
lig CH
lig
lig OH
H2O
H2O
xylograms

35. 35
Xylograms; different species
-1,25
-1
-0,75
-0,5
-0,25
0
am. cell OH
cell CH CH2
cell CH2
cell CO OH CH2
cell OH
cell OH CH CH2
cell OH CO
cryst. cell OH
cryst. cell CH
cryst. cell OH CH
cell hemi CH
cell hemi CH CH2 OH CO
cell hemi OH CH
cell hemi lig CH
extr. CH
hemi C=O
hemi CH
hemi CH
hemi OH
hemi CH
hemi lig extr. CH C=C C=O
lig CH
lig CH
lig CH
lig
lig OH
H2O
H2O
-1,25
-1
-0,75
-0,5
-0,25
0
am. cell OH
cell CH CH2
cell CH2
cell CO OH CH2
cell OH
cell OH CH CH2
cell OH CO
cryst. cell OH
cryst. cell CH
cryst. cell OH CH
cell hemi CH
cell hemi CH CH2 OH CO
cell hemi OH CH
cell hemi lig CH
extr. CH
hemi C=O
hemi CH
hemi CH
hemi OH
hemi CH
hemi lig extr. CH C=C C=O
lig CH
lig CH
lig CH
lig
lig OH
H2O
H2O
-1,25
-1
-0,75
-0,5
-0,25
0
am. cell OH
cell CH CH2
cell CH2
cell CO OH CH2
cell OH
cell OH CH CH2
cell OH CO
cryst. cell OH
cryst. cell CH
cryst. cell OH CH
cell hemi CH
cell hemi CH CH2 OH CO
cell hemi OH CH
cell hemi lig CH
extr. CH
hemi C=O
hemi CH
hemi CH
hemi OH
hemi CH
hemi lig extr. CH C=C C=O
lig CH
lig CH
lig CH
lig
lig OH
H2O
H2O
-1,25
-1
-0,75
-0,5
-0,25
0
am. cell OH
cell CH CH2
cell CH2
cell CO OH CH2
cell OH
cell OH CH CH2
cell OH CO
cryst. cell OH
cryst. cell CH
cryst. cell OH CH
cell hemi CH
cell hemi CH CH2 OH CO
cell hemi OH CH
cell hemi lig CH
extr. CH
hemi C=O
hemi CH
hemi CH
hemi OH
hemi CH
hemi lig extr. CH C=C C=O
lig CH
lig CH
lig CH
lig
lig OH
H2O
H2O
Black locust
(Robinia pseudoacacia)
no treated 160ºC 180ºC 200ºC 220ºC
Norway spruce
(Picea abies)
no treated 160ºC 180ºC 200ºC 220ºC
Silver fir
(Abies alba)
no treated 160ºC 180ºC 200ºC 220ºC
European larch
(Larix decidua)
no treated 160ºC 180ºC 200ºC 220ºC
Wild cherry
(Prunus avium)
no treated 160ºC 180ºC 200ºC 220ºC
European beech
(Fagus silvatica)
no treated 160ºC 180ºC 200ºC 220ºC
European ash
(Fraxinus excelsior)
no treated 160ºC 180ºC 200ºC 220ºC
-1,25
-1
-0,75
-0,5
-0,25
0
am. cell OH
cell CH CH2
cell CH2
cell CO OH CH2
cell OH
cell OH CH CH2
cell OH CO
cryst. cell OH
cryst. cell CH
cryst. cell OH CH
cell hemi CH
cell hemi CH CH2 OH CO
cell hemi OH CH
cell hemi lig CH
extr. CH
hemi C=O
hemi CH
hemi CH
hemi OH
hemi CH
hemi lig extr. CH C=C C=O
lig CH
lig CH
lig CH
lig
lig OH
H2O
H2O
-1,25
-1
-0,75
-0,5
-0,25
0
am. cell OH
cell CH CH2
cell CH2
cell CO OH CH2
cell OH
cell OH CH CH2
cell OH CO
cryst. cell OH
cryst. cell CH
cryst. cell OH CH
cell hemi CH
cell hemi CH CH2 OH CO
cell hemi OH CH
cell hemi lig CH
extr. CH
hemi C=O
hemi CH
hemi CH
hemi OH
hemi CH
hemi lig extr. CH C=C C=O
lig CH
lig CH
lig CH
lig
lig OH
H2O
H2O
-1,25
-1
-0,75
-0,5
-0,25
0
am. cell OH
cell CH CH2
cell CH2
cell CO OH CH2
cell OH
cell OH CH CH2
cell OH CO
cryst. cell OH
cryst. cell CH
cryst. cell OH CH
cell hemi CH
cell hemi CH CH2 OH CO
cell hemi OH CH
cell hemi lig CH
extr. CH
hemi C=O
hemi CH
hemi CH
hemi OH
hemi CH
hemi lig extr. CH C=C C=O
lig CH
lig CH
lig CH
lig
lig OH
H2O
H2O
-1,25
-1
-0,75
-0,5
-0,25
0
am. cell OH
cell CH CH2
cell CH2
cell CO OH CH2
cell OH
cell OH CH CH2
cell OH CO
cryst. cell OH
cryst. cell CH
cryst. cell OH CH
cell hemi CH
cell hemi CH CH2 OH CO
cell hemi OH CH
cell hemi lig CH
extr. CH
hemi C=O
hemi CH
hemi CH
hemi OH
hemi CH
hemi lig extr. CH C=C C=O
lig CH
lig CH
lig CH
lig
lig OH
H2O
H2O
Sessile oak
(Quercus peraea)
no treated 160ºC 180ºC 200ºC 220ºC

36. 36
PLS models for TM softwoods
y = 0.9522x - 0.1186
R
2
= 0.944
-10
-8
-6
-4
-2
0
2
-10 -8 -6 -4 -2 0 2
reference
estimated
y = 0.9782x + 0.218
R
2
= 0.9662
6
7
8
9
10
11
12
13
6 7 8 9 10 11 12 13
reference
estimated
Mass loss Equilibrium Moisture Content

37. 37
TM of poplar veneers
78910111213141516171819
-0,00001
0,000005
54505950645069507450
wavenumber (cm-1
)
2
nd
derofNIRabsorbance
NT
239ºC, 1h, 250mb
239ºC, 2h, 1000mb
240ºC, 6.6h, 250mb
#26
#0
#37
#20
#18
#9
#15
#0 NT
#26 155ºC, 1h, 250mb
#9 174ºC, 22h, 250mb
#20 210ºC, 1h, 250mb
#18 239ºC, 1h, 250mb
#15 239ºC, 2h, 1000mb
#37 240ºC, 6.6h, 250mb

38. 38
Wood sterilization
How to detect if the wood was appropriately
thermally treated?
• standard ISPM No15;
• Temperature: 56°C in the core of wood
• Treatment time: 30minutes

39. 39
ISPM-15
Low temperature thermal treatment of wood
35
40
45
50
55
60
65
70
75
80
85
90
95
100
105
35 40 45 50 55 60 65 70 75 80 85 90 95 100 105
treatment temperature, degC
predictedtemperature,degC
calibration
validation
• to investigate an effect of the temperature on
wood samples of different wood species:
• Spruce (softwood with resin)
• Fir (softwood without resin)
• Poplar (hardwood)
• different treatment times:
• 0.5 hours (30 minutes)
• 1 hour
• 3 hours
• 6 hours
• different treatment temperatures:
• 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95,
100°C
• three samples for repetition/statistics
• five-times spectra measurements for analysis
of weathering effect:
• Conditioned after treatment
• Measured after 1month
• Measured after 3 months
• Measured after 6 mounts
• Measured after 12 mounts
468 samples measured x 5 spectra/sample x 5 repetition/sample = 9360 spectra

40. 40
NIR-based method for verification of the
ISPM-15 treatment*







      
      
changestothespectrachangestothespectra
NO TREATED
TREATED
Spectra
+
model

41. 41
891011121314
-0,000015
-0,000005
0,000005
55005600570058005900600061006200630064006500
wavenumber (cm
-1
)
2
nd
derofabsorbance
spruce reference
Coniophora puteana
Trametes versicolor
Wood decay
a cb
Trametes versicolor Postia placenta Coniophora puteana
y = 0.95x + 1.97
R2
= 0.93
-10
0
10
20
30
40
50
60
-10 0 10 20 30 40 50 60
reference mass loss, %
masslosspredictedwithNIR,%

42. 42
Decay type identification
Postia placenta
reference
Trametes versicolor
Coniphora puteana
Gloeophyllum trabeum

43. 43
Archaeological wood
Q
1 Q
2
Q
3Q
4
Q
5 Q
6
700-2700 years old

44. 44
Short term waterlogging
OAK
band assessment pine oak
nr
wave number (cm
-1
) wood component functional group peat water peat water
1 4195 lignin not assigned ○ ○  ◦
2 4268 cellulose CH, CH2 ○ ○ ○ ○
3 4401 cellulose, hemicelluloses CH, CH2, OH, CO ○ ○  ○
4 4546 lignin CH, C=O   ◦ 
5 4608 cellulose, hemicelluloses not assigned   ◦ 
6 4686 hemicelluloses, lignin, extractives CH, C=C, C=O    
7 4739 cellulose OH ○ ○ ○ ○
8 4808 cellulose semi-crystalline and crystalline OH, CH ○  ○ ○
9 5051 water OH   ○ ○
10 5198 water OH center of the range ○ ○ ○ ○
11 5245 hemicelluloses C=O    
12 5495 cellulose OH, CO   ◦ ◦
13 5593 cellulose semi-crystalline and crystalline CH ◦ ○  ◦
14 5666 not assigned CH, CH2   ◦ ○
15 5692 not assigned CH2    
16 5800 hemicelluloses (furanose / pyranose) CH ○ ○  
17 5865 hemicelluloses CH ○ ○ ◦ ○
18 5935 lignin CH ◦ ○ ○ ◦
19 5980 lignin CH   ○ ○
20 6126 cellulose OH ◦ ○  
21 6286 cellulose crystalline OH ◦ ◦ ◦ ◦
22 6334 cellulose OH    
23 6472 cellulose crystalline OH ◦ ◦  
24 6715 cellulose semi-crystalline OH ◦ ◦ ◦ ◦
25 7003 amorphous cellulose, water OH ◦ ◦ ◦ ◦
26 7092 lignin, extractives OH    ○
360 samples measured x 3
spectra/sample = 1080
spectra
peat water
years of exposition
0 2 4 6 8 2 4 6 8 arch
PINE

45. 45
Paper & NIR
5% wheat
2nd day
7th day
14th day
5% rye 5% wheat 5% rye
Chaetomium globosum
Aspergillus niger,
Penicillium funiculosum, Trichoderma viride
5% wheat
2nd day
7th day
14th day
5% rye 5% wheat 5% rye
Chaetomium globosum
Aspergillus niger,
Penicillium funiculosum, Trichoderma viride
3240 samples measured
x 3 spectra/sample =
9720 spectra

46. 46
Effect of soil type
-0,00004
-0,00003
-0,00002
-0,00001
0
0,00001
0,00002
0,00003
4100 4200 4300 4400 4500 4600 4700 4800 4900 5000 5100 5200 5300 5400 5500
wavenumber, cm-1
2
nd
derivativeofabsorbance
0 weeks
forest soil 8 weeks
agricultural soil 8 weeks
sand 8 weeks
biodegradation of the recycled paper with addition of 5% wheat bran

47. 47
Effect of time
biodegradation of the recycled paper with addition
of 5% wheat bran in the forest soil
0
4
8
PCA analysis of NIR spectra of paper before
degradation tests and decomposed in forest soil for
4 and 8 weeks.
Note: spectral range: 11000-4150cm-1,
pre-processing: 2nd derivative + VN
-0,00004
-0,00003
-0,00002
-0,00001
0
0,00001
0,00002
0,00003
4100 4200 4300 4400 4500 4600 4700 4800 4900 5000 5100 5200 5300 5400 5500
wavenumber, cm-1
2
nd
derivativeofabsorbance
0 week
1 week
8 week

48. 48
PLS models for paper samples
samozerwalosc
y = 0.76x + 0.77
R2
= 0.80
-1
0
1
2
3
4
5
6
7
-1 0 1 2 3 4 5 6 7
reference value (km)
estimatedvalue(km)
porost
y = 0.75x + 0.80
R2
= 0.80
0
1
2
3
0 1 2 3
reference value (3-0 scale)
estimatedvalue(3-0scale)
breaking length growth degree

49. 49
Estimation of mechanical stress
y = 0,90x + 68,91
R
2
= 0,90
0
200
400
600
800
1000
1200
1400
1600
0 200 400 600 800 1000 1200 1400 1600
reference force (N)
predictedforce(N)
stresses

50. 50
why does it work?
wavenumber, cm-1
wavenumber, cm-1
w
avenumber,cm
-1
w
avenumber,cm
-1
wavenumber, cm-1 wavenumber, cm-1
wavenumber,cm-1
wavenumber,cm-1
            
a) c)
b) d) in the plastic fieldin the elastic field
2D synchronous spectra during
wood tension
4062
4193
4200
4231
4266
4281
4285
4362
4401
4544
4605
4632
4683
4737
4779
4794
4806
5049
5196
5234
5242
5462
5493
5520
5593
5616
5662
5689
5774
5793
5797
5813
5863
5871
5898
5932
5948
5959
5994
6002
6125
6187
6283
6333
6449
6468
6519
6619
6657
6700
6711
6738
6754
6769
6789
6796
6873
6943
6974
7001
7089
7213
7298
7313
7321
7352
7410
8158
8247
8652
8748
nontreated
normalize
firstderivative_5
firstderivative_15
firstderivative_25
firstderivative+normalization_5
firstderivative+normalization_15
firstderivative+normalization_25
secondderivative_5
secondderivative_15
secondderivative_25
secondderivative+normalization_5
secondderivative+normalization_15
secondderivative+normalization_25
Determination
coefficients (r2)
between NIR spectra
amplitude and tension
stresses in a relation
to the spectra
preprocessing method

51. 51
Identity test – coating recognition
Preprocessing: 2 derivative + vector
normalization, 9 smoothing points
Method: factorization
Regions (cm-1): 4135-4350, 5365-5520,
5800-6000, 6290-6480, 7000-7200
Water solvent based
products: #13, #26, #41
Organic solvent based
products: #11, #12, #42

52. 52
band: 4200-10000cm-1, 2 derivative, 5 smoothing points, vector normalization, 2 factors
Identification of coatings
■ Resotec
● Nordwal
■ Solas
▼ Adler aquawood
● Adler pullex
▲ Adler pullex oil
Product #1
Product #2
Product #3
Product #4
Product #5
Product #6

53. 53
natural oak waxed oak varnished oak
Falejowkaoak
Antique wooden floors

54. 54
Monitoring of weathering
8 softwood, 3 hardwood, 12 exotic, 3 thermo modified wood
21 various water/organic based products with synthetic/natural/acrylic/alkyd resins/oils

55. 55
Monitoring wood weathering
-0.000006
0
55005600570058005900600061006200630064006500
wavenumber (cm-1
)
2
nd
derofabsorbance
reference
1 year
2 years
3 years
4 years
-OHcristallinecelulose
-CHsemi-cristallinecelulose
-OHcristallinecelulose
-CHlignin
-CHlignin
-CHhemicellulose
-CHaliphaticchains

56. 56
Short term weathering of wood
891011121314
-0,000002
0
0,000002
55005600570058005900600061006200630064006500
wavenumber (cm
-1
)
2
nd
derofabsorbance
-CH S-cr cell-CH He-CH Al ch-OH Cr cell -CH L -CH S-cr cell-CH He-CH Al ch-OH Cr cell -CH L
28 days
Wind

57. 57
Service life prediction
PCA of NIR spectra
of non coated
samples exposed to
South (4 years of
natural weathering);
 Reggio Emilia
 Roma
 Loninghens
 Macerata
 Udine  Trento
 Lecce  standard
EN927-6
EN927-6
EN927-6
1year
2years
2years
2years
1year
1year
Timemachine
Timemachine
Timemachine
0years
0years
0years
4years
4years
4years
EN11507
EN11507
EN11507
3years
3years
3years
EN927-6
EN927-6
EN927-6
1year
2years
2years
2years
1year
1year
Timemachine
Timemachine
Timemachine
0years
0years
0years
4years
4years
4years
EN11507
EN11507
EN11507
3years
3years
3years
a

58. 58
Research directions
spatial measurement
Hyperspectral Imaging
in field measurement
portable equipment

59. 59
Weathered wood
a) b)
PCA analysis performed on not weathered (a)
and sample after 28 days of weathering (b)
0 1 2 4 7 9 11 14 17 21 24 28

60. 60
K-means classification
K‐means clustering on the mosaic of weathered wood after pre‐selecting 3 (a),
4 (b) and 5 classes (c)
b) c)A B C D
E F G H
I J K L I
A B C D
E F G H
J K L
A B C D
E F G H
I J K L
a)

61. 61
NIR for log/biomass quality index in
mountain forest
wood measurement at various harvesting moments

62. 62
Wood defects detection
range 12500-5900cm-1
2nd derivative + VN

63. 63
Calibration transfer
y = 0.93x + 1.86
R² = 0.93
25,0
27,0
29,0
31,0
33,0
25,0 27,0 29,0 31,0 33,0
Measured total lignin content (%)
NIR‐predicted total lignin content (%)
y = 0.93x + 1.86
R² = 0.93
y = 0,99x + 0,31
R
2
= 0,86
25,0
27,0
29,0
31,0
33,0
25,0 27,0 29,0 31,0 33,0
Measured total lignin content (%)
NIR‐predicted total lignin content (%)
measurement of samples with
equipment #1
measurement of selected
samples with equipment #2equipment #2 model
development based on samples
measured with instrument #1
model #1 development
calculation of transfer
matrix
selection of
calibration samples
by Kennard Stone
algorithm

64. 64
Conclussions
NIR technique has been successfully used for:
• species and provenance recognition
• estimation chemical composition, physical and mechanical
properties
• monitoring chemical changes in wood during thermal modification,
weathering, waterlogging and decay
• recognition and classification of coatings
• characterization of archeological wood and cultural heritage objects
• service life prediction of wooden elements and structures
• paper characterization
• in-field applications
Current developments in the fields of optics and electronics open
new application for NIR. Its non-destructive character and simple
measurements allows assisting experts in the estimation of
material properties in a fast and repeatable way

65. 65
Thank you very much